Methods summary: Applying CRISPR to detect eDNA

Article by Molly-Ann Williams a PhD student at Dublin City University

We were challenged to design and build a simple and rapid species monitoring system. Why do we need such a system?  Biodiversity loss is at an all-time high and such a system would help to support the management and conservation of fish species within aquatic environments by acquiring knowledge of species distribution that traditionally is gained through visual detection and counting. These methods are expensive, time consuming and can lead to harm of the species of interest.    We decided that environmental DNA (eDNA) was the way to go but we had to solve the ‘PCR problem’ i.e., avoid having to do cyclical high temperatures as that would see us ending up with a costly, once-off device that would likely not be applied outside our lab.  This got us brainstorming and led us to a novel isothermal detection method, combining Recombinase Polymerase Amplification with CRISPR-Cas detection, which simplifies the adaptation of nucleic acid detection on to a biosensor device.

This innovative methodology utilises the collateral cleavage activity of Cas12a, a ribonuclease guided by a highly specific single CRISPR RNA, to detect specific species from eDNA. We proved it could work for eDNA by applying the technology to the detection of Salmo salar from eDNA samples collected in Irish rivers, where presence or absence had been previously confirmed using conventional field sampling. The beauty of this advance is that it can be applied to any species in the environment.  Not only does this assay solve the ‘PCR problem’, it is also is a better approach for distinguishing very closely related species.  We look forward to others in the field adapting it to their own favourite species of interest.  

Citation: Williams, M‐A, O’Grady, J, Ball, B, et al. The application of CRISPR‐Cas for single species identification from environmental DNA. Mol Ecol Resour. 2019; 19: 1106– 1114. https://doi.org/10.1111/1755-0998.13045

Summary from the authors: Boomeranging around Australia: Historical biogeography and population genomics of the anti-equatorial fish Microcanthus strigatus (Teleostei: Microcanthidae)

Photo credit: Shigeru Harazaki.

The study of species and where they live is of particular interest to biologists, because it not only allows us to gain insight into genetic diversity, but also into how different populations interact. Animals with widespread distributions are often assumed to be of least concern. This can be misleading, as it does not take into account the possibility of fragmentation and population disjunction. The Stripey fish Microcanthus strigatus is one example, as it is listed as being of least concern on the IUCN Red List. Although it spans a wide distribution across the western Pacific and eastern Indian Oceans, our study suggests that populations in Western Australia, the southwest Pacific (including eastern Australia), Hawaii and East Asia are very genetically divergent. Several of these populations have been isolated since the last glacial cycle in the Pleistocene epoch, and are currently so fragmented that no contemporary genetic exchange occurs. This is of significant conservation concern as a once widespread population is revealed to consist of four cryptic groups, especially in light of evidence suggesting that the Hawaiian population is currently in decline and that the southwest Pacific population is distinct enough to warrant recognition as a different species. 

Read the full article:
Tea Y‐K, Van Der Wal C, Ludt WB, Gill AC, Lo N, Ho SYW. Boomeranging around Australia: Historical biogeography and population genomics of the anti‐equatorial fish Microcanthus strigatus (Teleostei: Microcanthidae). Mol Ecol. 2019;28:3771–3785. https://doi.org/10.1111/mec.15172

National-scale eDNA metabarcoding study reveals diversity patterns of plant pathogens and how they change with land use

Plant pathogens are a major factor in farming and forestry, and also play a key role in ecosystem health. Understanding pathogens at national scales is critical for appropriate prevention and management strategies and for a sustainable provision of future ecosystem services and agroecosystem productivity. Despite this, at present we have little knowledge of the diversity patterns of plant pathogens and how they change with land use at a broad scale.

Photo credit: Ian Dickie

In our study we show how land uses such as farming and plantation forestry affected the variety of plant pathogens in soil, roots and on plant leaves – and we show there are many more species of plant pathogens in land that’s been modified by pasture, cropping, and plantation forestry than there are in natural forest. The patterns of pathogen diversity are distinct from other microbes.

These are some of the first landscape level insights into these critically important communities including fungal, oomycete and bacterial pathogens in seemingly healthy ecosystems. Our results give scientists new insights into where pathogens exist, and how pathogen communities are structured.

Andreas Makiola and Ian Dickie (Bio-Protection Research Centre, New Zealand)

Read the full article here

Summary from the authors: 31° South: The physiology of adaptation to arid conditions in a passerine bird

Karoo scrub-robin (Cercotrichas coryphaeus) in its typical arid habitat in southern Africa. Photo by Krista N. Oswald.

Written by Ângela M. Ribeiro

Arid environments are ecosystems of energetic stringency. Their typical high temperatures, low primary productivity, and unpredictable water availability prove physiologically challenging for birds. How these vertebrates cope with such harshness remains a conundrum in physiological evolutionary biology. While physiological adaptation likely involves energetic metabolic phenotypes, the underlying mechanisms (plasticity, genetics) are largely uncharacterized. To explore this, we developed a intra-specific level framework (Figure 1) that links environmental conditions, phenotypes and genotypes in a passerine bird whose range spans an aridity gradient. We found variation in energetic physiology phenotypes (a measure of energy expenditure) and gut microbiota composition (involved in energy retrieval from food) to be associated with environmental features and identified a small list of candidate adaptive genes. By working at the interface of physiology and genomics, we suggest that selective pressures on energetic physiology mediated by genes related to energy homeostasis and possibly with contribution of gut microbiota may facilitate adaptation to local conditions. Ultimately, our findings offer a possible explanation to the high avian intra-specific divergence observed in harsh environments, raises awareness that accounting for intra-specific variation is fundamental when modeling physiological responses to climate change, and provides a stepping-stone for further research into the mechanisms of phenotypic adaptation to aridity.

Figure 1. Conceptual framework to infer the mechanisms of physiological adaptation to aridity: linking environment (climate and primary productivity), phenotype (organism-level energetic metabolism: basal metabolic rate and metabolic expansibility; microbiome composition) and genotype (genetic variation in genes underlying the biochemical machinery of energy production).

Link to paper: https://onlinelibrary.wiley.com/doi/full/10.1111/mec.15176

Ribeiro ÂM, Puetz L, Pattinson NB, Dálen L, Deng Y, Zhang G, da Fonseca RR, Smit B, Gilbert MT. (2019). 31° South: The physiology of adaptation to arid conditions in a passerine bird. Molecular Ecology. 2019. 28-16. 3709-3721.

Intra-specific variation and the algal microbiome

Individuals within a species vary, and this variation can have important implications for the role a species may play within ecosystems. We compared the relative importance of variation within species due to genetic changes within its own genome versus symbiotic interactions between the focal species and its associated bacteria, also called their microbiome. We focused on Microcystis aeruginosa, a globally distributed photosynthetic cyanobacterium, also known as blue-green algae, that often dominates freshwater harmful algal blooms.

Colony of Microcystis aeruginosa from Gull Lake. Colony photographed by O. Sarnelle of Michigan State University and image prepared by John Megahan of University of Michigan.

These blooms have recently become more common and intense worldwide, causing major economic and ecological damages. We studied Microcystis and their associated microbiomes from lakes in Michigan, USA that vary in phosphorus content, which is the primary limiting nutrient in lakes. We found genomic changes among strains of Microcystis along this phosphorus gradient that indicated increased efficiency in the use of phosphorus and nitrogen. Intriguingly, we found that genotypes adapted to different nutrient environments co-occurred in phosphorus‐rich lakes. This co-occurrence may have critical implications for understanding how Microcystis blooms persist for many months, long after nutrients become depleted within lakes. Similar to previous findings in for example the human microbiome, we uncovered that the bacteria comprising the microbiomes of Microcystis varied in community composition but were more stable at the level of functional contributions to their hosts across the phosphorus gradient. Finally, while our work was mostly focused on unraveling the genomic underpinnings of nutrient adaptation, we also observed consequences of these differences in Microcystis genome and microbiome composition at a physiological level. In particular, when nutrients were provided in abundance, Microcystis (and its microbiome) that had evolved to thrive in low-phosphorus environments could not grow as rapidly as strains from high-phosphorus environments.

Sara Jackrel, Postdoctoral Fellow, University of Michigan.

Read the full article here.

Citation: Jackrel, SL, White, JD, Evans, JT, et al. Genome evolution and host‐microbiome shifts correspond with intraspecific niche divergence within harmful algal bloom‐forming Microcystis aeruginosaMol Ecol. 2019; 28: 3994– 4011. https://doi.org/10.1111/mec.15198

Summary from the authors: Selection at behavioural, developmental and metabolic genes is associated with the northward expansion of a successful tropical colonizer

Link to paper: https://onlinelibrary.wiley.com/doi/10.1111/mec.15162

Picture: Green Anole Lizard (Anolis carolinensis) on railing in Hilo, Hawaii. Author: Paul Hirst. CC-BY-SA-2.5

The green anole (Anolis carolinensis), also called the American chameleon due to its ability to change color, is a common species in South-East USA. It has been studied for decades to understand how reptiles adapt to their environment.  Unlike other species of its genus, its range encompasses territories outside tropical climate, reaching the winter-exposed flanks of the Appalachians. The green anole colonized these colder regions from Florida in the last 300,000 years. We used DNA variation covering the whole genome and contrasted populations having recently colonized colder territories with the ones from tropical Florida. We compared multiple approaches to detect which segments in DNA sequences harbored variation compatible with selection. Since these signatures can also be produced by past demography, we took the latter into account to limit the detection of false positives. We then identified the most likely function of genes overlapping with candidate regions for selection, and observed that many of those were involved in exploratory behavior, immunity and response to cold. This suggests that the success of green anoles may have been due to changes in both physiology and behavioral shifts, a hypothesis that could be further tested experimentally.

Yann Bourgeois and Stephane Boissinot

Bourgeois, Y., & Boissinot, S. (2019). Selection at behavioral, developmental and metabolic genes is associated with the northward expansion of a successful tropical colonizer. Molecular Ecology. 2019. 28-15. 3523-3543

Method summary: Mapping genetic patterns across landscapes with PHYLIN

            The spatial representation of species’ data is needed in most areas of biodiversity related research. In fact, mapping the species’ continuum to guide the prioritization of areas for conservation was the main driver for PHYLIN development, but the possible application is far more vast.

            Spatial representation of distances between georeferenced samples is challenging. The PHYLIN input are distance matrices and a table of samples classified in groups (lineages, for instance) with locations. PHYLIN relates a matrix measuring a particular distance between samples (for example, a genetic distance) with a matrix representing spatial distance between the same samples. PHYLIN then applies a kriging interpolation: models the relation by means of a variogram and uses that information as weights to interpolate to other locations a probability of belonging to each of the groups

Different applications of PHYLIN with randomly generated data. a) using a simple euclidean distance with 3 dimensions is possible to interpolate over 3d environments; b) using a layer of climate as resistance to movement it is possible to analyse the impact of climate change on connectivity; c) using a Jaccard distance matrix instead of genetic distance to map the contact zone between two species (click on the image for source code).

The latest version of PHYLIN adds the possibility of using multiple spatial distance metrics, opening an exciting avenue with different applications. In our recent paper in Molecular Ecology Resources, we showed how different mechanisms of genetic isolation can be represented in space by PHYLIN. The application of the method is not limited to that and we show here three other possible applications: using 3 dimensional distance (similarly to an ocean environment), climate change connectivity and species distributions/contact zone.

Pedro Tarroso, Guillermo Velo-Antón and Silvia Carvalho  

See the full paper here: https://onlinelibrary.wiley.com/doi/abs/10.1111/1755-0998.13010

A step by step tutorial can be found here: https://cran.r-project.org/web/packages/phylin/vignettes/phylin_tutorial.pdf